Dust Reduction in Bauxite Waste: Role of Gypsum, Carbonation, and Microbial Decomposition
Bibliographic record
Abstract
Producing alumina by the Bayer Process creates fine air borne red dust which devalues property and causes irritation to the human respiratory system. Aggregation of such inorganic particles was proposed as a dust-inhibiting corrective. Resistance to breakdown under simulated rain suggests a lower number of dust-size particles after rain. Samples of red mud waste (1) treated 10 years before the study at the 0-15 cm depth zone with 40 t ha-1 of gypsum (2) from the subjacent 15-30 cm zone, were collected, crushed and passed through a 0.5 mm diameter sieve. Leaves from Acacia senensis (a legume) were finely chopped to < 1 mm and thoroughly mixed with the sieved bauxite waste at 25- and 50%, and the samples incubated for 6 weeks at ambient room temperatures, at 60% soil water-holding capacity. To determine the fraction of potential dust, the treated samples were submerged in de-ionized water for several days until there was no change in discoloration (due to clay dislocation) of the water. The samples were removed from the water and the water evaporated and the residues dried and weighed. In total, the dust-reducing capabilities of the treatments in descending order of proficiency were: 50% phytogenic > 25% phytogenic > 0-15 cm soil depth non-phytogenic > 15-30 cm-depth non-phytogenic. The 50% phytogenic-treatment reduced potential particles of fugitive dust by 70% over the untreated controls and 95% over the crushed-only (subjacent red mud; no organics added) samples. All in all, phyto-organics increased average particle size to > 100 µm by flocculation, thereby creating stable agglomerates which resisted disintegration and breakdown under simulated rain. Reducing the concentration of < 75 μm particles in the air will decrease morbidity due to respiratory illnesses in surrounding populations, harmful effects on vegetation, and the defacement of buildings. This treatment promises the use of gypsum + phyto-organics for reducing the emanation of surface dust from red mud waste sites onto surrounding areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".